The present invention generally relates to diagnostics of Electro-Mechanical Actuators (EMA) motoring subsystems, and more particularly, to variable structure-integrated diagnostic approach to achieve low-frequency data sampling for EMA motoring diagnostic subsystems.
There has been an increase in application of EMA and Electro-Hydrostatic Actuators (EHA) to flight and mission critical systems, such as spacecraft, military air vehicles and commercial aircraft. (Hereinafter, for simplicity, the term “EMA” refers to both EMA and EHA.) A diagnostic and prognostic capability of EMA is important since this capability enhances the system reliability of a mission and flight critical systems (i.e., flight control) which improves mission reliability and ensures safe in-flight operation. The concept is applicable to a broad range vehicles and systems that operate on land, sea or air and make use of EMA technology.
Prior art EMA motor diagnostics are based on additional or dedicated hardware circuits to collect and process the machine electrical variables. However, it is very desirable to develop integrated diagnostics for the EMA motoring or drive subsystem. This invention presents a novel approach of integrated diagnostics, motoring and adaptation system, using the embedded electronic circuit that already exists in the system for the EMA drive control. The technology described here also applies to situations were the EMA diagnostics must be implemented external to the embedded electronics but the local sources for computation, data collection and communications to move the necessary data to another processing element(s) are very limited.
Recent findings in the integrated diagnostics show that significant technical challenges must be overcome to meet various flight requirements for many new and valuable applications, such as Unmanned Combat Air Vehicles (UCAV).
One of the significant challenges stems from the limitations of the existing embedded microprocessor or DSP (digital signal processor) circuits. These processors are dedicated to control functions and have very limited available computational time that can be spared for diagnostics versus the high-priority real-time digital control tasks. The time-sharing of the real-time resources at high machine speeds (flight platform maneuvers which translates to EMA cycle activity) may be quite different from that of low machine speeds. At high machine speeds, the hardware has, effectively, a much shorter time frame to complete the same amount of data sampling and processing that is essential for the diagnostics. Herein, essential tasks for diagnostics may include continuous collecting, processing and evaluating substantial amount of machine data in a very short period. Alternatively, if the overall diagnostic processing is moved external to the embedded electronics this increases the requirement for additional communications resources.
Typically, the electrical motors that drive an EMA, such as Brushless-DC (BLDC) machines, are designed to operate at a high base frequency of the stator fundamental current. The higher the base operating frequency, the lower the volume and weight of the machine can be. The speed of a typical BLDC motor in an aircraft EMA system can range from 9,000 to 15,000 rpm. Therefore, there has arisen a need for diagnostics methodology to achieve low-frequency data sampling for EMA that operates at high base frequency. Furthermore, to optimize the low-frequency data sampling and processing at different mission phases or activity of the aircraft, the diagnostic and motoring system methodology should be able to select and identify the proper operational mode of the platform.
Another issue stems from the potential degrading of the accuracy of machine modeling at high machine speeds. As an example, a model-based approach can be applied to detect the early phase of machine's bearing surface wear based on the increase of the rotational resistant torque on the motor shaft. At high machine speeds, the machine's windage losses can be non-negligible and can affect the accuracy of the model-based approach. Therefore, there has arisen a need for diagnostic methodology that is immune to the effects of windage loss and can provide accurate diagnostic output while using limited computing and/or communications resources.
As can be seen, there is a critical need to develop new diagnostics methodology and embedded electronic system that enables optimized low-frequency data sampling and reduced data processing rate for EMA motoring subsystems. This new approach and techniques reduce the cost of processing and communications hardware required to implement the diagnostics and prognostics in real time. The new diagnostics methodology and system select and identify the proper operational mode of EMA positioning control during different mission phases to optimize the low-frequency data sampling and processing. Moreover, the new approach of a diagnostics and prognostics methodology allows EMA motoring subsystems to detect the incipient failure symptoms and predict a pending failure mode of an electrical machine and actuator in the subsystems. Furthermore, such diagnostics methodology can be extended to estimate the remaining machinery life within a reasonable statistical confidence bound, which will greatly reduce the risk of any untimely failure or downtime during the operation, assure the mission readiness, facilitate timely and cost effective system maintenance, reduce the life-cycle cost (LCC), and reduce the turn around time of the subsystems.